Cryptocurrency has emerged as a lucrative yet volatile landscape for cybercriminal activity, presenting novel challenges for law enforcement and policymakers alike. This review seeks to explore the diverse array of cybercrimes occurring within the cryptocurrency domain, examining their types, motives, techniques, and the regulatory responses shaping this complex ecosystem. Utilizing a scoping literature search methodology, this study analyzes 228 pertinent sources drawn from a pool of over 4,000 reviewed publications. The findings elucidate the intricate interplay between cryptocurrencies and illicit activities, revealing the multifaceted nature of cybercrimes within this realm. From the exploitation of the dark web for illicit transactions to the pervasive threat of crypto ransomware targeting entities globally, the review underscores the diverse methods and motivations driving such nefarious endeavors. By shedding light on the evolving tactics employed by cybercriminals and exploring future directions for technological and regulatory measures adopted by governments, this paper offers valuable insights to navigate this dynamic landscape effectively.
Adam L. Hooker, Wenjian Huang, Shalini Kapali Kurumathur, Nishant Vishwamitra · 5 authors
The (ab)use of encryption and compression in hiding illegal digital content complicates efforts by law enforcement agencies (LEAs) to procure evidence to support the elements of proof required in criminal prosecution. This reinforces the importance of designing solutions to determine the file type of an encrypted file (e.g., videos and still images in the context of illegal picture investigations), which can be used to build probable cause in a court order application to have the file decrypted. While machine learning (ML) has shown immense capabilities in several detection tasks, the suitability of ML for detecting file types in encrypted or compressed files has not been explored. Furthermore, since detecting file types in real-world LEA applications is a high-stake decision-making problem, existing ML techniques that do not provide prediction uncertainty are not as useful. In this work, we take the first step toward detecting file types in encrypted or compressed files using ML for LEA applications based on their Byte Frequency Distributions (BFD). We then compose a dataset1of BFDs of 300,000 encrypted and compressed data from 12,000 diverse files for five different file types. We conduct an in-depth analysis of our dataset and demonstrate the utility of ML techniques in detecting file types of content in encrypted and compressed files based on BFDs. Informed by these findings, we present our proposed framework, eDefender, designed to facilitate the detection of file types in encrypted and compressed files for LEA applications, by employing uncertainty quantification of detection scores based on ensembling. eDefender successfully flags directories with encrypted or compressed image/video-type files with an F1-score of 90.7%.
Smart contracts have caused significant economic losses due to various vulnerabilities, highlighting the importance of their security. Traditional vulnerability detection methods often focus on a single aspect, either neglecting the semantic and structural information in the source code or overlooking critical execution changes in the bytecode. This can result in incomplete security assessments. To address this, we propose HCBERT (HybridCodeBERT), a pre-trained hybrid model for smart contract vulnerability detection. HCBERT efficiently ex- tracts features from both source code and bytecode by fusing their information and integrating attention mechanisms with graph-based structures such as data flow and control flow graphs. Additionally, leveraging advanced pre-training techniques, we enhance the model's ability to capture complex patterns. Experimental results show that HCBERT achieves detection accuracies of 89.57%, 82.91 %, 94.73%, and 85.79% for four key vulnerabilities: reentrancy, delegate call, timestamp dependency, and overflow/underflow, outperforming existing state-of-the-art methods. Ablation experiments further confirm the benefits of feature fusion and the integration of graph structures and pre-training techniques in improving detection performance.
WebAssembly (WASM) has emerged as a crucial technology in smart contract development for several blockchain platforms. Unfortunately, since their introduction, WASM smart contracts have been subject to several security incidents caused by contract vulnerabilities, resulting in substantial economic losses. However, existing tools for detecting WASM contract vulnerabilities have accuracy limitations, one of the main reasons being the coarse-grained emulation of the on-chain data APIs. In this article, we introduce WACANA, an analyzer for WASM contracts that accurately detects vulnerabilities through fine-grained emulation of on-chain data APIs. WACANA precisely simulates both the structure of on-chain data tables and their corresponding API functions, and integrates concrete and symbolic execution within a coverage-guided loop to balance accuracy and efficiency. Evaluations on a vulnerability dataset of 2,012 contracts show WACANA outperforming state-of-the-art tools in accuracy. Further validation on 5,602 real-world contracts confirms WACANA’s practical effectiveness.
Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard
The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity. This growth, however, is accompanied by significant security risks such as illicit accounts engaged in fraud. Effective detection is further limited by the scarcity of labeled data and the evolving tactics of malicious accounts. To address these challenges with a robust solution for safeguarding the DeFi ecosystem, we propose $\textbf{SLEID}$, a $\textbf{S}$elf-$\textbf{L}$earning $\textbf{E}$nsemble-based $\textbf{I}$llicit account $\textbf{D}$etection framework. SLEID uses an Isolation Forest model for initial outlier detection and a self-training mechanism to iteratively generate pseudo-labels for unlabeled accounts, enhancing detection accuracy. Experiments on 6,903,860 Ethereum transactions with extensive DeFi interaction coverage demonstrate that SLEID significantly outperforms supervised and semi-supervised baselines with $\textbf{+2.56}$ percentage-point precision, comparable recall, and $\textbf{+0.90}$ percentage-point F1 -- particularly for the minority illicit class -- alongside $\textbf{+3.74}$ percentage-points higher accuracy and improvements in PR-AUC, while substantially reducing reliance on labeled data.
Web 3.0 technologies such as blockchain, cryptocurrency, and Non-Fungible Tokens (NFTs) are progressively making waves. Despite their adoption in various sectors, their application in the education sector, particularly in Higher Educational Institutions (HEIs), is largely unexplored. Our research proposes the development of a secure NFT platform using blockchain technology, tailored for HEIs. The primary aim of this research is to authenticate rewards, and certificates, and digitalize badges using NFTs, minimizing potential fraud and enhancing data security. Reward NFTs can enhance students' and teachers' career recognition and credit. EduNFT is a ‘first of its kind’ in the Sultanate of Oman in creating a Web 3.0 technology-enabled campus where students are rewarded with digital assets (NFTs) for their achievements by the teachers or the institution over an NFT platform in a secured environment. It runs on the Polygon blockchain which is a "layer two" or "sidechain" scaling solution that runs alongside the Ethereum blockchain - allowing speedy transactions with low fees. The research methodology incorporates an authentication process that requires users to log in via their crypto wallets which enhances security and maintains user privacy. Instead of traditional physical rewards, this digital approach allows achievements to be stored perpetually in the blockchain, reducing risks of loss or damage. The proposed research empowers an impressive expansion of NFT utilization in higher education, contributing to the ongoing growth of the NFT space. The outcome, a pioneering NFT platform powered by Web 3.0 technology, stands to revolutionize the higher education sector, not only in Oman but potentially worldwide. In addition, the research serves as a crucial step toward integrating contemporary technologies into educational systems and furthering their evolution.
D. Rosy Salomi Victoria, S. Roselin Mary, K. Somasundaram
Evident Guard is the best way to add credibility to the evidence in the court. It includes the First Information Report (FIR) system, SHA-1 algorithm, and secure attachment of evidence. This system revolutionizes the current mode of an investigation that is based on trustworthiness, and positive law such as transparency and honesty. After linking FIR with a safe evidence app; users will be able to upload their own proof directly thus enhancing the transparency of the investigation process. Pivotal contents will be in an instant glance for endurance by case involved groups. In the SHA-1 cryptographic algorithm, the alteration of the original program will be detected as a result of the hash changing its value along an iteration number. The program hashes each piece of evidence and delivers them as well-defined hash values hence the data getting unique fingerprints. Cryptography used the representations of the encoding technique to verify that original version data had no definite hash values. The system stores the hashed data instead of the centralized databases. The proof of the FIR-report evidence became real-time and tamper resistant with the blockchain, which is a technology which has made it possible to guarantee their unbreakable nature. The use of Non-Fungible Tokens (NFT) as part of blockchain technology is a proof for safeguarding the whole process. NFTs digitize evidence into unique digital goods that certify the ownership and truthfulness of the evidence.
The blockchain technology is fast becoming a crucible of e governance. It is pitched to safeguard privacy and is hard to tamper with as it works using the distributed ledger technology. The digital evidence is secured and strengthened respectively. The regulators are dealing with complex and immense volume of data. To deal with this challenge, one of the possible solutions is cloud computing. The use of blockchain in digital forensics has initiated nuanced understanding of evidence management. There are multiple storage and classification models related to blockchain along with algorithms that are helpful in data handling. This article is an analysis of the evolving models that use blockchain and algorithms. The purpose of this article is to analyse and suggest sustainable and pragmatic solutions for regulators. The algorithmic approaches discussed in this article are efficient in terms of time, cost, access and energy. However, to further the objectives of National Strategy on Blockchain, enhanced efforts are needed to secure digital evidence management.
J. V. Anchitaalagammai, S. Kavitha, S. Murali, J. Janci Rani · 6 authors
Blockchain is the technology through which distributed ledger ensures the secure, transparent, and tamper-proof recording of information across a decentralized network. Its need arises from growing concerns over data security, privacy, and the risk of unauthorized alterations, especially in institutions handling sensitive information, such as libraries. Libraries today face huge risks, the most common being hacking, data breaches, and unauthorized manipulation of data. The use of blockchain technology will ensure assets by augmenting the integrity of data, hence records are changed and not reliable. The paper explores the technical features of blockchain: how it can be used in decentralizing and also ensuring immutability, and consequently protect library systems from data tampering and ensure more transparency in the processes. It also analyzes the successes and shortcomings of blockchain-related applications in other fields, inspiring the belief that this technology can really make the library systems stronger and enhance further the safety and reliability of their business procedures.
Public blockchains and distributed ledger technologies (DLTs) provide reliable distributed, decentralized datasharing, with consensus mechanisms and cryptographic security, data integrity, transparency and trust. Though this transparency and open access allows malicious actors to record arbitrary information to the DLT by exploiting free-form editable text fields in request headers or transaction bodies. The authors propose an architecture that runs external agents interfaced with blockchains, capable of filtering unwanted content to mitigate this threat. Implementation of a resource description framework (RDF) based verification mechanism for blockchain input fields, and an alternate approach using natural language processing (NLP) are then described. Finally, the efficiency and effectiveness of the solution is demonstrated on a blockchain deployment.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The non-fungible token (NFT) is a record used to ensure the privilege and ownership of a certain digital asset with a distinctive digital identifier in a blockchain. In digital-multimedia assets i.e. images, videos, audio, etc. NFTs generally retain references to the properties of the asset. Due to the nature of blockchain technology, NFT is oftentimes associated with its immutability. NFTs that represent digital multimedia assets may utilize blockchain-based file servers, but oftentimes time the associated file is stored off-chain. This makes the assets themselves vulnerable to link rot and causes the NFT owner to lose access to their assets since the off-chain storageis not immutable like the NFT. To preserve the integrity of the NFT’s associated digital assets, we introduce Abridged Neat Key Instance (ANKI). ANKI is a property embedded inside NFT that contains the seed number of the digital multimedia asset to be procedurally generated. We are taking advantage of Procedural Content Generation (PCG) technology to reproduce the digital multimedia asset presented by the information available inside the NFT itself. By leveraging Procedural Content Generation(PCG) technology, our approach can regenerate the exact digital asset directly from the information encoded within the NFT.In our experiments, we successfully recreated music files using the ANKI seed and a PCG engine, verifying their accuracy by comparing the MD5 and SHA-512 checksums of the generated files with those of the original NFT assets. All 50 generated musicfiles produced identical checksums to their original counterparts,confirming the integrity of our method.
Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance.
The emergence of Web3 technologies presents new challenges and implications for investigative techniques and evidence acquisition in the field of digital forensics. This research paper delves into the intricate landscape of Web3 and its impact on the investigative processes involved in digital forensics. By examining the unique characteristics of Web3, such as decentralized networks, blockchain technology, and smart contracts, this study aims to highlight the complexities that digital forensic investigators face in this rapidly evolving environment.Through a comprehensive review of existing literature, this paper identifies key challenges in conducting digital investigations within Web3 ecosystems. These challenges include issues related to data integrity, privacy concerns, jurisdictional boundaries, and the authentication of digital evidence. Furthermore, the implications of these challenges on traditional investigative techniques are discussed, emphasizing the need for adaptation and innovation in digital forensic practices.Overall, this research sheds light on the evolving nature of digital forensics in the context of Web3 technologies, providing insights for forensic practitioners, law enforcement agencies, and policymakers to better navigate and address the complexities of investigating digital crimes in decentralized and blockchain-based environments.
As the adoption of cryptocurrencies continues to grow, so does the complexity and scale of financial crimes involving digital assets. The need for a robust crypto crime investigation tool has never been more critical. Blockstash Intelligence is an advanced solution designed to meet this demand by equipping law enforcement agencies, financial institutions, and compliance teams with the tools necessary to track, analyse, and combat illicit activities in the crypto space. Our platform supports multiple blockchains, including Bitcoin (BTC), Ethereum (ETH), and Tron(TRX), offering a wide-ranging capability to investigate across different cryptocurrency ecosystems. Blockstash Intelligence provides a suite of features such as graph visualization of crypto transactions, real-time transaction monitoring, and compliance capabilities. With real-time off-chain to on-chain data mapping, path generation from wallets to exchanges, and comprehensive wallet assessments, Blockstash Intelligence is a helpful resource for tracing and investigating cryptocurrency-related crimes.
There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards described in natural languages. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors systematically injected by us to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing.
As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation.
Open access
Advanced Steganography and Watermarking Techniques
Blockchain technology, celebrated for its decentralized architecture and promise of transparency, has also become a conduit for illicit activities. This paper introduces a novel rule-based methodology for detecting illegal transactions within blockchain networks. By analyzing transaction data through parameters such as block range, transaction hashes, and addresses, the approach identifies suspicious patterns including high-value transactions, unusual fee structures, and links to known illicit addresses. Implemented in Python, the methodology has been rigorously tested and validated, achieving an accuracy of $88 \%$ with strong precision, recall, and $F 1$ scores. This research advances the field of cybersecurity, regulatory compliance, and law enforcement by providing a practical and computationally efficient framework for detecting illegal activities in Bitcoin transactions. The rule-based framework, utilizing both threshold-based and heuristic rules, offers valuable tools for financial institutions, law enforcement, and regulators to identify and investigate illicit transactions effectively. The study underscores the need for proactive measures in protecting digital ecosystems and proposes future enhancements, including advanced anomaly detection and realtime analysis, to further improve the framework’s efficacy and adaptability.
Symbolic execution has proven effective for code analytics in smart contracts. However, for smart contracts, existing symbolic tools use multiple-transaction symbolic execution, which differs from traditional symbolic tools and also exacerbates the path explosion problem. In this paper, we first quantitatively analyze the bottleneck of symbolic execution in multiple transactions (TXs), finding the redundancy of the paths of TXs. Based on this finding, we propose LENT-SSE as a new speculation heuristic for Speculative Symbolic Execution of smart contracts, which leverages the executed and near TXs for skipping and recalling the SMT solving of paths. LENT-SSE uses an executed-transaction-based skipping algorithm to reduce the time required for SMT solving by leveraging the redundancy between executed and executing paths. Moreover, LENT-SSE uses a near-transaction-based recalling algorithm to reduce false skipping of the solving paths. Experimental results on the SmartBugs dataset show that LENT-SSE can reduce the total time by 37.4% and the solving time of paths by 65.2% on average without reducing the reported bugs. On the other dataset of 1000 realistic contracts, the total time and solving time are reduced by 38.1% and 54.7%.
Daniele Friolo, Geoffrey Goodell, D. R. Toliver, Hazem Danny Nakib
This article builds upon the protocol for digital transfers described by Goodell, Toliver, and Nakib, which combines privacy by design for consumers with strong compliance enforcement for recipients of payments and self-validating assets that carry their own verifiable provenance information. We extend the protocol to allow for the verification that reissued assets were created in accordance with rules prohibiting the creation of new assets by anyone but the issuer, without exposing information about the circumstances in which the assets were created that could be used to identify the payer. The modified protocol combines an audit log with zero-knowledge proofs, so that a consumer spending an asset can demonstrate that there exists a valid entry on the audit log that is associated with the asset, without specifying which entry it is. This property is important as a means to allow money to be reissued within the system without the involvement of system operators within the zone of control of the original issuer. Additionally, we identify a key property of privacy-respecting electronic payments, wherein the payer is not required to retain secrets arising from one transaction until the following transaction, and argue that this property is essential to framing security requirements for storage of digital assets and the risk of blackmail or coercion as a way to exfiltrate information about payment history. We claim that the design of our protocol strongly protects the anonymity of payers with respect to their payment transactions, while preventing the creation of assets by any party other than the original issuer without destroying assets of equal value.
Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.
The expansion of decentralized cryptocurrencies poses notable complexities for law enforcement in terms of detecting unlawful behaviors, as well as in the identification of individuals and the retrieval of transaction histories of perpetrators who take advantage of the pseudonymous nature inherent in the cryptocurrency system. This research paper puts forth a solution called Kavach. The designated user of Kavach will be an investigator. The tool leverages graph machine learning for the categorization of transactions as illicit or legitimate. It utilizes graph-based embeddings for the recognition of potentially suspicious addresses within the bitcoin network. The tool incorporates a predefined watch list for such suspicious addresses and will have the capability to trace the digital trail of these addresses using Open Source Intelligence (OSINT).